Allianz DataViz Challenge

library(plotly)
library(data.table)
library(tidyr)
library(knitr)
library(heatmaply)

Preprocessing

• Load data file
• rename genres for better readability
  • “Religion, Spirituality & New Age” to “Religion”
  • “Science.fiction” to “SciFi”
  • “Action.and.Adventure” to “Action”

All genres:

 [1] "Satire"        "SciFi"         "Drama"         "Action"        "Romance"       "Mystery"       "Horror"       
 [8] "Self.help"     "Health"        "Guide"         "Travel"        "Children.s"    "Religion"      "Science"      
[15] "History"       "Math"          "Anthology"     "Poetry"        "Encyclopedias" "Dictionaries"  "Comics"       
[22] "Art"           "Cookbooks"     "Diaries"       "Journals"     

• Check if upper and lower triangle identical

[1] TRUE

• Transform to long and tidy data.table

head(books_dt)

• Average number of genres per customer

[1] 2.332187

First ideas

Show me everything!

• Romance, SciFi, Action, History are most bought
• bought-together clusters:
  • Romance, SciFi, Action, History
  • Dictionaries and Comics
  • Math and Poetry
• Mystery is an outlier

Most bought genre

Best pairs

• mostly combinations of most bought genres

Special genres

Hypothesis

• If a customer buys more than 2 genres, he is recorded in more than 1 off-diagonal entry:
  • (2*diagonal - colSum) < 0
• If a genre is bought more often alone than in triplets (or higher):
  • (2*diagonal - colSum) > 0

Look for customers that buy only one genre

• Compare column sum and 2*diagonal value
• generate table with {genre, {2*diagonal-colSum}}

Normalize columns by value of diagonal

With clustering of rows and columns

Relative best pairs